Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviours over time. As a solution to this problem, we evaluate the use of three different strategies that allow the periodic rediscovery or incremental construction of the predictive model so as to exploit new available data. The evaluation focuses on the performance of the new learned predictive models, in terms of accuracy and time, against the original one, and uses a number of real and synthetic datasets with and without explicit Concept Drift. The results provide an evidence of the potential of incremental learning algorithms for predicting process monitoring in real environments.
翻译:现有调查周密的预测程序监测技术通常根据以往的处决程序建立一个预测模型,然后利用它预测新的进行中案件的未来,而不可能在完成执行后用新的案件来更新它。这会使预测程序监测过于僵化,无法处理在现实环境中工作的各种过程的变异性,这些过程随着时间的推移不断演变和/或表现出新的变异行为。作为解决这个问题的办法,我们评估了三种不同战略的使用情况,这些战略允许定期重新发现或逐步建立预测模型,以便利用新的现有数据。评价的重点是新的已学习的预测模型在准确性和时间方面与最初的模型相比的性能,并使用一些真实的和合成的数据集,而没有明确的“概念”Drift。结果证明了在实际环境中预测过程监测过程的递增学习算法的潜力。